How CPA Firms Can Step Up in the Age of AI and Automation

Across industries, the pace of technological change has entered overdrive. Companies — from nimble startups to sprawling multinationals — are rapidly adopting Artificial Intelligence (AI) and Robotic Process Automation (RPA) to streamline operations, enhance decision-making, and reduce costs. While these tools unlock immense value, they are also creating gaps in oversight, governance, and risk management that auditors can no longer ignore.

AI is now being used to analyze complex datasets, generate forecasts, detect fraud, and even write business content (think tools like ChatGPT). RPA, on the other hand, automates repetitive tasks such as invoice processing, report generation, and system reconciliations; particularly in finance, HR, and procurement.

These technologies are fast, scalable, and relatively inexpensive to implement. But as adoption surges, internal controls, governance frameworks, and audit practices are struggling to keep up. This presents a growing risk — not only to the companies deploying these technologies, but also to the CPA firms tasked with assuring their integrity.

As auditors, we are being pulled into uncharted waters. The audit profession must evolve if it is to continue providing relevant, trusted assurance in a digital-first world.

Real-World Lessons from Governance Failures

To understand the stakes, consider two prominent real-world examples where AI systems were deployed without sufficient oversight — resulting in reputational and human harm.

Case 1: The Dutch Tax Authority Scandal

In one of Europe’s most high-profile algorithmic governance failures, the Dutch tax authority deployed an AI system to detect welfare fraud. The algorithm disproportionately flagged low-income and immigrant families, many of whom were falsely accused and financially devastated as a result. Investigations revealed that biased assumptions and a lack of human oversight allowed the system to operate unchecked for years. The scandal ultimately forced the resignation of the Dutch government in 2021.

Audit Lesson: AI systems can embed and amplify existing societal biases. Without independent validation, ethical review, and auditability, such systems risk undermining both fairness and legal compliance.

Case 2: Amazon’s Hiring Bias

In a widely publicized case, Amazon developed an internal AI-powered recruitment tool intended to streamline candidate selection. However, the tool began penalizing resumes that included the word “women’s” and deprioritized graduates from all-women’s colleges. The issue? The model had been trained on data which mostly of it favored male applicants. 

Audit Lesson: Training data is not always neutral; especially when it reflects historical inequalities. Auditors must challenge the fairness, validity, and transparency of AI systems, especially when used in hiring, lending, pricing, or performance evaluation.

The Audit Risk Landscape

With AI and RPA increasingly integrated into critical business processes, auditors face new types of risks that aren’t covered by traditional audit methodologies. If we fail to ask the right questions or understand how these tools work, we risk missing material misstatements, flawed controls, or undetected fraud.

Clients aren’t just digitizing operations; they’re automating judgment. As stewards of public trust, auditors must ensure that governance keeps pace with innovation.

How Audit Firms Can Respond

1. Ask the Right Questions

Audit planning should now include inquiries such as:

• Is the client using AI for financial forecasts, fraud detection, or hiring decisions?
• Are RPA bots interacting with core financial systems, and are they bypassing manual controls?
• Who is responsible for AI oversight? Is there executive ownership or board-level reporting?

2. Demand Audit Trails

Ensure AI and RPA systems maintain logs, approvals, and exception records. This is especially crucial for areas such as:

• Revenue recognition
• Journal entries
• Contract interpretation
• Risk scoring and customer segmentation

3. Evaluate Governance Frameworks

Review whether the client has:

• A formal AI/RPA governance policy
• Risk assessments before deployment
• Testing protocols for algorithm updates or re-training

4. Adapt Your Audit Methodology

Modernize your audit procedures to:

• Integrate AI and automation risks into internal controls testing
• Use data analytics to detect anomalies introduced by bots or AI models
• Challenge automated assumptions embedded in management estimates

5. Upskill and Collaborate

This is not a solo effort. Firms must:

• Cross-train audit staff in basic AI concepts and risks
• Involve cybersecurity and IT audit specialists
• Partner with external experts in machine learning and data governance when needed

The Strategic Advantage for Audit Firms

AI and RPA are not temporary fads; they are the new infrastructure of modern business. But if left unchecked, they can embed silent errors, amplify bias, and erode internal control environments.

Clients are increasingly seeking assurance providers who understand both financial integrity and digital risk. This presents a clear opportunity for forward-looking CPA firms to lead from the front. By investing in digital audit capabilities today, firms can:

• Reduce future audit failures
• Enhance client trust and retention
• Differentiate themselves in a crowded market

Technology Can’t Govern Itself

The audit profession has always been about bridging confidence and accountability. In this new era of smart machines and autonomous systems, that mandate is more relevant than ever.

Let’s not be caught flat-footed. Let’s lead. Start with a trusted audit tool here.